13.10.2013 | Workshop

International News Recommender Systems and Challenge (NRS 2013)

October 13, 2013, Hong Kong, China

News article recommendation differs in several way from other well-known types of recommender systems such as for music and movies. The main differences are: (i) freshness is sometimes more important than relevancy, (ii) similarity between news articles does not necessarily mean they are related–unrelated despite news articles might share many words, (iii) the unstructured format of a news story is more difficult to analyze than other objects with structured properties such as a friends network, (iv) news readers might have special preference on some particular events contained in news articles, (v) serendipities (i.e., variety in recommended news articles) are of particularly importance, and (vi) breaking and trendy news articles might be of interest even if they are not related to the user’s general interests. In addition, recommending news articles entails special requirements regarding scalability. Continuously, a considerable amount of novel news articles enters the system. In contrast, movie recommender systems face a limited number of novel items. The mobile web allows users to navigate the Web using wireless devices such as smart phones. In recent years, we have witnessed a significant increase in the use of mobile data content such as music, movie information and news. This rapid growth of mobile data traffic will likely continue in the near future as well. Particularly, mobile technology is often referred to as a game-changer for news consumption (Reuters, Nov. 12, 2010). According to the latest Pew Research Center survey covering the changing news landscape, the proliferation of mobile devices and social networks is accelerating the shift to online news consumption. In the survey, 33 percent of mobile phone owners read newspapers on their mobile and 37 percent of internet users disseminate news content via postings on social media sites such as Facebook (www.facebook.com) and Twitter (www.twitter.com).

Despite the significant progress of recommender systems in general, there are still challenges that limit the effectiveness of currently available solutions in terms of news recommendation for mobile users. For example, the following challenges are significant in the domain of news recommendation: (a) mobile devices screens have limited space available to user interfaces, (b) news articles have short life cycles, (c) the challenge of cold-start users when they first request a recommendation and their interests are initially unknown, (d) the challenge of cold-start news articles, which refers to the difficulty of recommending new articles that have not been tied to many users’ preferences, (e) explicit signals about which news articles a user truly wishes to see are typically weak and the user’s desirability for a news article cannot simply be detected, (f ) there has not yet established a consent on how to evaluate a news recommender systems, and (g) users’ preference for particular articles depends not only on the topic and on propositional content, but also on users’ current context. A user’s current context can cover various types of information like the user’s current location, access time, social environment, and external events.